Swati Mishra
ABSTRACT
Interactive tools help users filter relevant information from massive online sources, like news feeds and online discussion forums, by enabling them to externalize their preferences. However, users’ information goals and preferences are often complex and are comprised of data attributes and a user’s subjective judgements over these attributes. For instance, when filtering news articles based on their newsworthiness, the system must capture both data attributes like recency and shareability of the article, along with the user’s personal and flexible assessment of news sentiment. While most interactive tools enable users to externalize goals that are expressible as true/false statements, they do not support incorporating subjective, loosely structured judgements of data attributes which fulfill complex goals. In this paper, we introduce Teachable Facets (TF), widgets that users can create on the fly to filter relevant information to improve the sense-making of analysts. These teachable widgets employ a Machine Teaching (MT) framework to enable users to formulate personalized filtering criteria for complex, multi-dimensional, loosely indexed, and unstructured data; teach a filtering criterion using representative samples; apply these filters to new data streams; and assess the relevance of outcomes. Through a user study, we evaluate the performance of these filters based on their ability to discover relevant items and the expressibility they offer to the users in teaching criteria. In our discussion, we identify ways this approach might improve future systems and delineate implications should such systems be deployed broadly.
- Christopher Ahlberg and Ben Shneiderman. 1994. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, Massachusetts, USA) (CHI ’94). Association for Computing Machinery, New York, NY, USA, 313–317. https://doi.org/10.1145/191666.191775Google Scholar
Digital Library
- Stuart Berg, Dominik Kutra, Thorben Kroeger, Christoph N Straehle, Bernhard X Kausler, Carsten Haubold, Martin Schiegg, Janez Ales, Thorsten Beier, Markus Rudy, 2019. Ilastik: interactive machine learning for (bio) image analysis. Nature methods 16, 12 (2019), 1226–1232.Google Scholar
- Laura Ana Maria Bostan, Evgeny Kim, and Roman Klinger. 2020. GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception. In Proceedings of the Twelfth Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 1554–1566. https://aclanthology.org/2020.lrec-1.194Google Scholar
- Mark Boukes, Natalie P Jones, and Rens Vliegenthart. 2022. Newsworthiness and story prominence: How the presence of news factors relates to upfront position and length of news stories. Journalism 23, 1 (2022), 98–116. https://doi.org/10.1177/1464884919899313 arXiv:https://doi.org/10.1177/1464884919899313Google ScholarCross Ref
- Helen Caple and Monika Bednarek. 2016. Rethinking news values: What a discursive approach can tell us about the construction of news discourse and news photography. Journalism 17, 4 (2016), 435–455.Google ScholarCross Ref
- Joseph Chee Chang, Nathan Hahn, Adam Perer, and Aniket Kittur. 2019. SearchLens: Composing and Capturing Complex User Interests for Exploratory Search. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 498–509. https://doi.org/10.1145/3301275.3302321Google ScholarDigital Library
- Shuo Chang, Peng Dai, Lichan Hong, Cheng Sheng, Tianjiao Zhang, and Ed H. Chi. 2016. AppGrouper: Knowledge-Based Interactive Clustering Tool for App Search Results. In Proceedings of the 21st International Conference on Intelligent User Interfaces (Sonoma, California, USA) (IUI ’16). Association for Computing Machinery, New York, NY, USA, 348–358. https://doi.org/10.1145/2856767.2856783Google ScholarDigital Library
- Po-Huan Chiu, Gloria Yi-Ming Kao, and Chi-Chun Lo. 2010. Personalized blog content recommender system for mobile phone users. International Journal of Human-Computer Studies 68, 8 (2010), 496–507. https://doi.org/10.1016/j.ijhcs.2010.03.005 Measuring the Impact of Personalization and Recommendation on User Behaviour.Google ScholarDigital Library
- Nicholas Cummins, Shahin Amiriparian, Sandra Ottl, Maurice Gerczuk, Maximilian Schmitt, and Björn Schuller. 2018. Multimodal Bag-of-Words for Cross Domains Sentiment Analysis. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Calgary, AB, Canada, 4954–4958. https://doi.org/10.1109/ICASSP.2018.8462660Google ScholarDigital Library
- Per-Erik Danielsson. 1980. Euclidean distance mapping. Computer Graphics and Image Processing 14, 3 (1980), 227–248. https://doi.org/10.1016/0146-664X(80)90054-4Google ScholarCross Ref
- Abhinandan S. Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google News Personalization: Scalable Online Collaborative Filtering. In Proceedings of the 16th International Conference on World Wide Web (Banff, Alberta, Canada) (WWW ’07). Association for Computing Machinery, New York, NY, USA, 271–280. https://doi.org/10.1145/1242572.1242610Google ScholarDigital Library
- Sarah Dean, Sarah Rich, and Benjamin Recht. 2020. Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 436–445. https://doi.org/10.1145/3351095.3372866Google ScholarDigital Library
- John J. Dudley and Per Ola Kristensson. 2018. A Review of User Interface Design for Interactive Machine Learning. ACM Trans. Interact. Intell. Syst. 8, 2, Article 8 (jun 2018), 37 pages. https://doi.org/10.1145/3185517Google ScholarDigital Library
- Efthimis N. Efthimiadis. 1993. A User-Centred Evaluation of Ranking Algorithms for Interactive Query Expansion. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Pittsburgh, Pennsylvania, USA) (SIGIR ’93). Association for Computing Machinery, New York, NY, USA, 146–159. https://doi.org/10.1145/160688.160710Google ScholarDigital Library
- Blaž Fortuna, Carolina Fortuna, and Dunja Mladenić. 2010. Real-Time News Recommender System. In Machine Learning and Knowledge Discovery in Databases, José Luis Balcázar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 583–586.Google Scholar
- Dominic Girardi, Josef Kueng, and Andreas Holzinger. 2015. A Domain-Expert Centered Process Model for Knowledge Discovery in Medical Research: Putting the Expert-in-the-Loop. In Brain Informatics and Health, Yike Guo, Karl Friston, Faisal Aldo, Sean Hill, and Hanchuan Peng (Eds.). Springer International Publishing, Cham, 389–398.Google Scholar
- Benjamin Gras, Armelle Brun, and Anne Boyer. 2016. Identifying Grey Sheep Users in Collaborative Filtering: A Distribution-Based Technique. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization (Halifax, Nova Scotia, Canada) (UMAP ’16). Association for Computing Machinery, New York, NY, USA, 17–26. https://doi.org/10.1145/2930238.2930242Google ScholarDigital Library
- Asela Gunawardana and Guy Shani. 2009. A Survey of Accuracy Evaluation Metrics of Recommendation Tasks. J. Mach. Learn. Res. 10 (dec 2009), 2935–2962.Google Scholar
- Tony Harcup and Deirdre O’Neill. 2017. What is News?Journalism Studies 18, 12 (2017), 1470–1488. https://doi.org/10.1080/1461670X.2016.1150193 arXiv:https://doi.org/10.1080/1461670X.2016.1150193Google ScholarCross Ref
- M. A. Hearst. 2006. Design recommendations for hierarchical faceted search interfaces. In Proc. SIGIR 2006, Workshop on Faceted Search. Association for Computing Machinery, Seattle, WA, 26–30.Google Scholar
- Marti A. Hearst and Chandu Karadi. 1997. Cat-a-Cone: An Interactive Interface for Specifying Searches and Viewing Retrieval Results Using a Large Category Hierarchy. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (Philadelphia, Pennsylvania, USA) (SIGIR ’97). Association for Computing Machinery, New York, NY, USA, 246–255. https://doi.org/10.1145/258525.258582Google ScholarDigital Library
- Bernie Hogan. 2015. From Invisible Algorithms to Interactive Affordances: Data After the Ideology of Machine Learning. Springer International Publishing, Cham, 103–117. https://doi.org/10.1007/978-3-319-05467-4_7Google ScholarCross Ref
- Andreas Holzinger, Markus Plass, Michael Kickmeier-Rust, Katharina Holzinger, Gloria Cerasela Crişan, Camelia-M. Pintea, and Vasile Palade. 2019. Interactive Machine Learning: Experimental Evidence for the Human in the Algorithmic Loop. Applied Intelligence 49, 7 (jul 2019), 2401–2414. https://doi.org/10.1007/s10489-018-1361-5Google ScholarDigital Library
- Shih-Wen Huang, Pei-Fen Tu, Wai-Tat Fu, and Mohammad Amanzadeh. 2013. Leveraging the Crowd to Improve Feature-Sentiment Analysis of User Reviews. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (Santa Monica, California, USA) (IUI ’13). Association for Computing Machinery, New York, NY, USA, 3–14. https://doi.org/10.1145/2449396.2449400Google ScholarDigital Library
- Muhammad Ibrahim and Mark Carman. 2016. Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank. ACM Trans. Inf. Syst. 34, 4, Article 20 (aug 2016), 38 pages. https://doi.org/10.1145/2866571Google ScholarDigital Library
- Suraya Nurain Kalid, Keng-Hoong Ng, Gee-Kok Tong, and Kok-Chin Khor. 2020. A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes. IEEE Access 8 (2020), 28210–28221. https://doi.org/10.1109/ACCESS.2020.2972009Google ScholarCross Ref
- Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng-Keen Wong. 2013. Too much, too little, or just right? Ways explanations impact end users’ mental models. In 2013 IEEE Symposium on Visual Languages and Human Centric Computing. IEEE Institute of Electrical and Electronics Engineers, San Jose, CA, USA, 3–10. https://doi.org/10.1109/VLHCC.2013.6645235Google ScholarCross Ref
- Andrew Kuznetsov, Joseph Chee Chang, Nathan Hahn, Napol Rachatasumrit, Bradley Breneisen, Julina Coupland, and Aniket Kittur. 2022. Fuse: In-Situ Sensemaking Support in the Browser. In Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology (Bend, OR, USA) (UIST ’22). Association for Computing Machinery, New York, NY, USA, Article 34, 15 pages. https://doi.org/10.1145/3526113.3545693Google ScholarDigital Library
- Lei Li, Li Zheng, Fan Yang, and Tao Li. 2014. Modeling and broadening temporal user interest in personalized news recommendation. Expert Systems with Applications 41, 7 (2014), 3168–3177. https://doi.org/10.1016/j.eswa.2013.11.020Google ScholarDigital Library
- Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. In 2008 Eighth IEEE International Conference on Data Mining. IEEE, Pisa, Italy, 413–422. https://doi.org/10.1109/ICDM.2008.17Google ScholarDigital Library
- Jiahui Liu, Peter Dolan, and Elin Rønby Pedersen. 2010. Personalized News Recommendation Based on Click Behavior. In Proceedings of the 15th International Conference on Intelligent User Interfaces (Hong Kong, China) (IUI ’10). Association for Computing Machinery, New York, NY, USA, 31–40. https://doi.org/10.1145/1719970.1719976Google ScholarDigital Library
- Ping Liu, Karthik Shivaram, Aron Culotta, Matthew A. Shapiro, and Mustafa Bilgic. 2021. The Interaction between Political Typology and Filter Bubbles in News Recommendation Algorithms. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). Association for Computing Machinery, New York, NY, USA, 3791–3801.Google ScholarDigital Library
- Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg, and Le Song. 2017. Iterative Machine Teaching. In Proceedings of the 34th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 2149–2158. https://proceedings.mlr.press/v70/liu17b.htmlGoogle Scholar
- Mykola Makhortykh and Mariëlle Wijermars. 2021. Can Filter Bubbles Protect Information Freedom? Discussions of Algorithmic News Recommenders in Eastern Europe. Digital Journalism 0, 0 (2021), 1–25. https://doi.org/10.1080/21670811.2021.1970601 arXiv:https://doi.org/10.1080/21670811.2021.1970601Google ScholarCross Ref
- Gary Marchionini. 2006. Exploratory Search: From Finding to Understanding. Commun. ACM 49, 4 (apr 2006), 41–46. https://doi.org/10.1145/1121949.1121979Google ScholarDigital Library
- Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 1999. A Machine Learning Approach to Building Domain-Specific Search Engines. In Proceedings of the 16th International Joint Conference on Artificial Intelligence - Volume 2 (Stockholm, Sweden) (IJCAI’99). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 662–667.Google Scholar
- Swati Mishra and Jeffrey M. Rzeszotarski. 2021. Crowdsourcing and Evaluating Concept-Driven Explanations of Machine Learning Models. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 139 (apr 2021), 26 pages. https://doi.org/10.1145/3449213Google ScholarDigital Library
- Swati Mishra and Jeffrey M Rzeszotarski. 2021. Designing Interactive Transfer Learning Tools for ML Non-Experts. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 364, 15 pages. https://doi.org/10.1145/3411764.3445096Google ScholarDigital Library
- Swati Mishra and Jeffrey M Rzeszotarski. 2023. Human Expectations and Perceptions of Learning in Machine Teaching. In Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (Limassol, Cyprus) (UMAP ’23). Association for Computing Machinery, New York, NY, USA, 13–24. https://doi.org/10.1145/3565472.3595612Google ScholarDigital Library
- Toni-Jan Keith Palma Monserrat, Shengdong Zhao, Kevin McGee, and Anshul Vikram Pandey. 2013. NoteVideo: Facilitating Navigation of Blackboard-Style Lecture Videos. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Paris, France) (CHI ’13). Association for Computing Machinery, New York, NY, USA, 1139–1148. https://doi.org/10.1145/2470654.2466147Google ScholarDigital Library
- Raymond J. Mooney and Loriene Roy. 2000. Content-Based Book Recommending Using Learning for Text Categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries (San Antonio, Texas, USA) (DL ’00). Association for Computing Machinery, New York, NY, USA, 195–204. https://doi.org/10.1145/336597.336662Google ScholarDigital Library
- Xi Niu, Xiangyu Fan, and Tao Zhang. 2019. Understanding Faceted Search from Data Science and Human Factor Perspectives. ACM Trans. Inf. Syst. 37, 2, Article 14 (jan 2019), 27 pages. https://doi.org/10.1145/3284101Google ScholarDigital Library
- Maria Panteli. 2019. Recommendation Systems Compliant with Legal and Editorial Policies: The BBC+ App Journey. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 529. https://doi.org/10.1145/3298689.3346961Google ScholarDigital Library
- Jaakko Peltonen, Jonathan Strahl, and Patrik Floréen. 2017. Negative Relevance Feedback for Exploratory Search with Visual Interactive Intent Modeling. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (Limassol, Cyprus) (IUI ’17). Association for Computing Machinery, New York, NY, USA, 149–159. https://doi.org/10.1145/3025171.3025222Google ScholarDigital Library
- W.D. Penny and S.J. Roberts. 1999. Dynamic logistic regression. In IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), Vol. 3. IEEE, Washington, DC, USA, 1562–1567 vol.3. https://doi.org/10.1109/IJCNN.1999.832603Google ScholarCross Ref
- Robert Plutchik. 1980. Chapter 1 - A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION. In Theories of Emotion, Robert Plutchik and Henry Kellerman (Eds.). Academic Press, 3–33. https://doi.org/10.1016/B978-0-12-558701-3.50007-7Google ScholarCross Ref
- Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2022. News Recommendation with Candidate-Aware User Modeling. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 1917–1921. https://doi.org/10.1145/3477495.3531778Google ScholarDigital Library
- Hema Raghavan, Omid Madani, and Rosie Jones. 2006. Active Learning with Feedback on Features and Instances. J. Mach. Learn. Res. 7 (dec 2006), 1655–1686.Google Scholar
- Gonzalo Ramos, Christopher Meek, Patrice Simard, Jina Suh, and Soroush Ghorashi. 2020. Interactive machine teaching: a human-centered approach to building machine-learned models. Human–Computer Interaction 35, 5-6 (2020), 413–451.Google Scholar
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (Chapel Hill, North Carolina, USA) (CSCW ’94). Association for Computing Machinery, New York, NY, USA, 175–186. https://doi.org/10.1145/192844.192905Google ScholarDigital Library
- Tuukka Ruotsalo, Giulio Jacucci, and Samuel Kaski. 2020. Interactive faceted query suggestion for exploratory search: Whole-session effectiveness and interaction engagement. Journal of the Association for Information Science and Technology 71, 7 (2020), 742–756. https://doi.org/10.1002/asi.24304 arXiv:https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/asi.24304Google ScholarDigital Library
- Chirag Shah and Emily M. Bender. 2022. Situating Search. In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval (Regensburg, Germany) (CHIIR ’22). Association for Computing Machinery, New York, NY, USA, 221–232. https://doi.org/10.1145/3498366.3505816Google ScholarDigital Library
- Dev Shah, Haruna Isah, and Farhana Zulkernine. 2018. Predicting the Effects of News Sentiments on the Stock Market. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 4705–4708. https://doi.org/10.1109/BigData.2018.8621884Google ScholarCross Ref
- Patrice Y Simard, Saleema Amershi, David M Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, 2017. Machine teaching: A new paradigm for building machine learning systems. arXiv preprint arXiv:1707.06742 (2017).Google Scholar
- Daniel J. Simons and Daniel T. Levin. 1997. Change blindness. Trends in Cognitive Sciences 1, 7 (1997), 261–267. https://doi.org/10.1016/S1364-6613(97)01080-2Google ScholarCross Ref
- Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Adv. in Artif. Intell. 2009, Article 4 (jan 2009), 1 pages. https://doi.org/10.1155/2009/421425Google ScholarCross Ref
- Jennifer Wortman Vaughan. 2017. Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research.J. Mach. Learn. Res. 18, 1 (2017), 7026–7071.Google ScholarDigital Library
- Robert Villa, Nicholas Gildea, and Joemon M. Jose. 2008. FacetBrowser: A User Interface for Complex Search Tasks. In Proceedings of the 16th ACM International Conference on Multimedia (Vancouver, British Columbia, Canada) (MM ’08). Association for Computing Machinery, New York, NY, USA, 489–498. https://doi.org/10.1145/1459359.1459424Google ScholarDigital Library
- Geoffrey I Webb, Michael J Pazzani, and Daniel Billsus. 2001. Machine learning for user modeling. User modeling and user-adapted interaction 11 (2001), 19–29.Google Scholar
- Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2016. Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. IEEE Transactions on Visualization and Computer Graphics 22, 1 (jan 2016), 649–658. https://doi.org/10.1109/TVCG.2015.2467191Google ScholarDigital Library
- Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, and Xing Xie. 2019. NPA: Neural News Recommendation with Personalized Attention. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 2576–2584. https://doi.org/10.1145/3292500.3330665Google ScholarDigital Library
- Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023. Personalized News Recommendation: Methods and Challenges. ACM Trans. Inf. Syst. 41, 1, Article 24 (jan 2023), 50 pages. https://doi.org/10.1145/3530257Google ScholarDigital Library
- Chuhan Wu, Fangzhao Wu, Yongfeng Huang, and Xing Xie. 2023. Personalized News Recommendation: Methods and Challenges. ACM Trans. Inf. Syst. 41, 1, Article 24 (jan 2023), 50 pages. https://doi.org/10.1145/3530257Google ScholarDigital Library
- Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, and Ming Zhou. 2020. MIND: A Large-scale Dataset for News Recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 3597–3606. https://doi.org/10.18653/v1/2020.acl-main.331Google ScholarCross Ref
- Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. 2019. Explainable AI: A brief survey on history, research areas, approaches and challenges. In Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8. Springer, Springer, 563–574.Google ScholarDigital Library
- Meike Zehlike, Ke Yang, and Julia Stoyanovich. 2022. Fairness in Ranking, Part I: Score-Based Ranking. ACM Comput. Surv. 55, 6, Article 118 (dec 2022), 36 pages. https://doi.org/10.1145/3533379Google ScholarDigital Library
- Wenbin Zhang and Steven Skiena. 2010. Trading Strategies to Exploit Blog and News Sentiment. Proceedings of the International AAAI Conference on Web and Social Media 4, 1 (May 2010), 375–378. https://doi.org/10.1609/icwsm.v4i1.14075Google ScholarCross Ref
- Jian Zhao, Steven M. Drucker, Danyel Fisher, and Donald Brinkman. 2012. TimeSlice: Interactive Faceted Browsing of Timeline Data. In Proceedings of the International Working Conference on Advanced Visual Interfaces (Capri Island, Italy) (AVI ’12). Association for Computing Machinery, New York, NY, USA, 433–436. https://doi.org/10.1145/2254556.2254639Google ScholarDigital Library
- Xiaojin Zhu. 2015. Machine Teaching: An Inverse Problem to Machine Learning and an Approach toward Optimal Education. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (Austin, Texas) (AAAI’15). AAAI Press, 4083–4087.Google ScholarCross Ref
Index Terms
- Teachable Facets: A Framework of Interactive Machine Teaching for Information Filtering
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